Job description
We are looking for a Generative AI Platform Engineer to design, build, and scale enterprise-grade AI platforms and APIs that enable the adoption of large language models and generative AI capabilities across the organization. This role is focused on platform engineering, not just model experimentation — you will be responsible for building robust, secure, and highly scalable systems that integrate with leading cloud-based AI services. You will work closely with software engineers, data scientists, and product teams to deliver reusable AI infrastructure, developer-friendly APIs, and production-ready solutions.
Key Responsibilities
Design and build scalable Generative AI platforms, services, and APIs for internal and external consumers
Develop and maintain high-performance backend services using Python and one or more of C++, C#, or Java
Integrate and operationalize LLM and foundation model APIs, including: Azure OpenAI Google Vertex AI AWS Bedrock
Build abstraction layers and orchestration logic to support multiple model providers and deployments
Design RESTful and/or gRPC APIs with a strong focus on reliability, security, and performance
Implement platform capabilities such as: Prompt management and versioning Model routing and fallback strategies, Observability, logging, and monitoring Cost and usage tracking Deploy and operate services on Google Cloud Platform (GCP), leveraging managed services where appropriate Support CI/CD, infrastructure-as-code, and production operations
Contribute to platform architecture decisions and engineering best practices
Required Qualifications
7+ years of professional software engineering experience
Bachelors degree in Computer Science or a related field (Masters degree preferred)
Strong proficiency in Python Strong experience in at least one of the following: C++, C#, or Java
Proven experience building platforms, frameworks, and APIs (not just applications)
Hands-on experience with Google Cloud Platform (GCP)
Practical experience integrating with cloud-hosted AI/LLM APIs, including Azure OpenAI, Vertex AI, and/or AWS Bedrock
Strong understanding of API design, distributed systems, and cloud-native architectures
Experience taking systems from design through production deployment and operation
Preferred Qualifications
Experience designing multi-tenant or enterprise AI platforms
Familiarity with MLOps or LLMOps concepts (model lifecycle, monitoring, evaluation)
Experience with containerization and orchestration (Docker, Kubernetes)
Knowledge of authentication, authorization, and secure API design
Experience supporting developer platforms or internal tooling
What Were Looking For
A platform-first mindset — you enjoy building reusable systems that enable other teams
Strong engineering fundamentals and attention to production quality
Comfort working across cloud services, APIs, and distributed systems
Ability to collaborate with both technical and non-technical stakeholders
Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.